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The claim is that Schema makes you visible. So, we decided to test that.

AEO is basically SEO. And AEO’s are desperate to try to change that narrative, using conjecture and hocus pocus, smoke and mirror. Instead, we just did good ol’ SEO

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If you want to understand why your site isn’t ranking, you have to stop listening to the “Google Illuminati.”

About 20 years ago, the SEO industry split into two distinct camps. You need to understand this history, or you will spend your entire career parroting buzzwords you don’t understand.

The first group—let’s call them the Illuminati Believers—convinced themselves that Google was a god. They believed the algorithm could “see” and “know” everything, that it had an innate sense of quality, and that if you just “wrote for humans,” the rankings would follow.

The second group—the Math Realists—knew the truth. We understood that Google’s winning mechanism was PageRank. We knew that underneath the hood, the engine wasn’t a literary critic; it was a calculator. Its breakdown was mathematical, its simplicity was superb, and we played it for exactly what it was.

Two decades later, nothing has changed. But if you look at the modern state of SEO, you’d think we were living in a different universe.

The Rise of the “SEO Ideologue”

Today, the industry is overrun by people who grew up on the “Illuminati” worldview. They are allergic to the idea that PageRank is still the primary driver of search.

Instead of math, they sell you Confirmation Bias.

You see this every time a “content writer” claims their poetic prose is why a page ranked #1, ignoring the domain authority holding it up. They seek to re-affirm their pre-committed biases—that “Google loves content” or “Google loves great writing.”

Let’s be clear: Google is agnostic. It processes HTML output. It does not “love” your technical stack, it does not “admire” your React framework, and it certainly doesn’t “enjoy” your blog post. It scores them.

The New “Holy Grails” (GEO, LLMs, and Tech SEO)

Because the reality of SEO (building authority/links) is hard work, people are constantly inventing “unicorns”—special secret sauces that promise results without the grind.

1. The “Tech SEO” Trap

There is a prevailing myth that having perfectly validated Schema or a 100/100 PageSpeed score is “SEO.” It isn’t. It’s hygiene. Google reads the HTML output. Whether that output came from a headless CMS or a static HTML file from 1999 doesn’t matter to the crawler. Yet, agencies sell “Technical SEO” packages as if fixing a render-blocking script is going to double your traffic.

2. The “GEO” Disinformation Playbook

Now we have Generative Engine Optimization (GEO) and the rise of LLM SEO.

The new gurus will tell you that AI search engines are somehow “immune” to spammy backlinks. They use colorful, emotive language to convince you that LLMs.txt is the new meta-tag that will save you.

Figure 1: While gurus chase AI trends, the mathematical reality of the web remains a graph of authority nodes.

It’s a fantasy. LLMs are trained on the open web—spam and all. They don’t magically know your “topical authority” unless you have the links to prove it. “Intent matching” is just a fancy re-branding of keyword planning. LLMs.txt gives you zero advantage in ranking. It’s just another distraction.

The Uncomfortable Truth

Why does this “Primary Position” make people so angry? Why does merely mentioning PageRank evoke triggered downvotes and “LoL” responses on Reddit?

Because admitting that SEO is Math strips away the magic.

If you admit that SEO is essentially Marketing + Math, you have to admit that it requires hard work, strategy, and capital. You can’t just “tweak” your way to the top with a new plugin.

Here is the reality check you won’t get on LinkedIn:

• Google hasn’t changed. It is still a link-based retrieval system.

• New “techniques” are usually mirror-myths. Brand mentions, sentiment analysis, “helpfulness” scores—these are often just proxies for authority.

• You need to live through it. If you didn’t see the evolution from the front lines, you are likely just parroting techniques that stopped working in 2012.

Stop Chasing Unicorns

SEO is not about chasing the latest algorithm leak or optimizing for a robot that doesn’t exist yet. It is about creating a work model and sticking to it.

Stop looking for the special meta-tag. Stop believing that “content is king” in a vacuum.

The game is still PageRank. The game is still Authority.

Everything else is just noise.

Executive Summary

• Skepticism is a Skill: Question every new “trend” (like GEO or Tech SEO audits) by asking: “How does this impact the math?”

• Back to Basics: If your traffic is flat, don’t look at your LLMs.txt. Look at your referring domains.

• Ignore the Noise: The industry makes money by selling you “change.” You make money by realizing things stay the same.

The post SEO Strategy Hasn’t Changed in 20 Years – You’re Just Distracted by Myths appeared first on Primary Position SEO NYC.

An effective YouTube SEO strategy starts before you ever hit upload: it begins with understanding search intent, planning content around real queries, and then packaging each video so both humans and algorithms immediately “get” what it’s about. When this is done well and repeated consistently, YouTube will not only rank your videos in search, but also push them through “Suggested” and external impressions.

Step 1: Define goals and audience

Before tactics, get clear on the role YouTube plays in your overall marketing strategy. This keeps SEO decisions focused instead of chasing random trends.

• Decide the main goal: brand awareness, leads, product education, or direct sales support.

• Define your primary viewer: who they are, what problems they search for, and how YouTube fits into their journey (top-of-funnel learning vs. bottom-of-funnel evaluation).

Step 2: Do YouTube-focused keyword research

YouTube SEO starts with discovering the exact phrases people already use to find content like yours. These become the backbone of your video topics and series.

• Use YouTube’s search bar (and tools like TubeBuddy/vidIQ, if available) to gather autocomplete suggestions and long-tail variations around your niche.

• Group keywords into small topic clusters (for example: “YouTube SEO strategy”, “YouTube keyword research”, “YouTube titles and thumbnails”) and plan multiple videos per cluster.

Step 3: Plan content formats and structure

The same topic can be delivered through tutorials, comparisons, audits, or case studies; choose formats that match intent and keep viewers watching.

• For “how to” and “guide” queries, plan step-by-step tutorials, live audits, or “before/after” breakdowns.

• Outline each video with: a strong hook, a promise, 3–5 clear sections, and a recap/CTA to another related video or playlist.

Step 4: Optimize titles, thumbnails, and hooks

These three elements largely control click-through rate and early retention, which heavily influence rankings.

• Put the main keyword near the start of the title and pair it with an outcome or benefit (for example: “YouTube SEO Strategy: Rank Videos and Get Leads”).

• Design custom thumbnails with a clear focal point, high contrast, and a simple visual story that matches (not exaggerates) the title.

Step 5: Use descriptions, tags, and chapters as your “text layer”

YouTube reads your surrounding text to understand context and match your video to searches; think of this as on-page SEO for your video.

• In the first 2–3 sentences of the description, restate the core topic, who it’s for, and what problem it solves, naturally using your main keyword and a few related phrases.

• Add relevant tags and a small number of focused hashtags, and include chapters with descriptive, keyword-aware labels for each segment.

Step 6: Optimize the video content itself

The algorithm analyzes watch time, retention, and engagement; your content structure can directly influence these signals.

• Start fast: in the first 5–10 seconds, confirm what the video is about and why it’s worth watching all the way through.

• Remove filler, use pattern interrupts (visual changes, examples, screen shares), and refer back to upcoming sections so viewers have a reason to stay.

Step 7: Use transcripts, captions, and accessibility

Accurate text versions of your video help both discoverability and user experience.

• Upload a clean transcript or captions file instead of relying only on auto-captions, especially for technical topics or brand names.

• Make sure your spoken content actually uses key phrases and related terms naturally rather than relying solely on metadata.

Step 8: Build playlists and internal pathways

YouTube rewards channels that keep viewers on the platform, especially within your own content ecosystem.

• Group related videos into tightly themed playlists and include keywords in playlist titles and descriptions.

• Use end screens, info cards, and pinned comments to guide viewers from one video to the next in the same topic cluster.

Step 9: Promote strategically in the first 24–48 hours

Early engagement sends strong signals about relevance and quality.

• Share new uploads with your email list, social channels, and website audience, encouraging full watches and comments rather than low-quality, idle views.

• Embed videos in related blog posts or landing pages so they get steady, targeted traffic over time.

Step 10: Analyze, iterate, and double down

YouTube analytics is where your SEO strategy becomes a feedback loop instead of a one-time checklist.

• Track impressions, CTR, average view duration, retention graphs, and the search terms that actually drive views to each video.

• For videos with solid impressions but weak CTR, test new titles and thumbnails; for good CTR but poor retention, refine hooks, pacing, and structure in future uploads.

By following this framework repeatedly—research, plan, package, promote, and iterate—you turn YouTube into a predictable search and discovery channel rather than a guessing game, and every new video increases the authority and reach of the entire channel.

The post Crafting an SEO strategy for YouTube appeared first on Primary Position SEO NYC.

The best way to do SEO for YouTube as an agency is to treat each channel like a small, tightly focused website: build topic authority with series-based content, nail the “text layer” (titles, descriptions, transcripts) for clear intent, and then drive views and links from relevant external entities instead of chasing YouTube “hacks”. In other words, think less like a “YouTube growth hacker” and more like a technical SEO who happens to work inside YouTube’s ecosystem.

Start with search intent and topics

For clients, scope YouTube SEO around real queries and problems their buyers type into YouTube and Google, then build repeatable series rather than one-off uploads. Map keywords to specific “pages” in the YouTube universe (videos, playlists, and even Shorts series) instead of chasing generic subscriber or view milestones.

• Use YouTube autocomplete and competitor channels to build topic clusters (e.g. “QuickBooks for startups”, “B2B cold email teardown”, “Shopify CRO audits”).

• Plan episodic series so the channel becomes the best library on a tight niche, not a random content dump.

Treat descriptions like mini landing pages

YouTube’s algorithm relies heavily on text around the video to understand what it is, who it’s for, and when to surface it. That means your descriptions and titles should act like stripped‑down blog posts that clearly answer a search intent.

• Put the main keyword near the start of the title and opening line of the description, then write a concise, human-first summary using natural variations and related phrases.

• Add structure: timestamped sections, internal links to related videos/playlists, and a clear CTA, all reinforcing the same topic cluster.

Leverage transcripts and on-video signals

Because YouTube auto-generates transcripts and uses them for understanding content, the spoken script itself is part of your SEO surface. Make the machine’s job easy by being explicit, not clever.

• Say the target keyword, problem, and audience in the first 5–10 seconds (“In this video, we’re breaking down how B2B SaaS agencies can…”).

• Upload clean captions or a transcript, especially for technical or B2B content, so the indexable text is accurate and rich.

Build authority with entities and backlinks

From a Weblinkr-style perspective, real authority comes from entities and references, not tricks. For YouTube, that means using your client’s broader ecosystem—site, partners, PR—to send contextual signals and traffic into their videos.

• Embed videos on relevant blog posts, docs, landing pages, and partner sites so the channel is clearly tied into a brand, product, and topic graph.

• Pursue guest content and collaborations where your client’s videos become the canonical explainer (podcasts, SaaS marketplaces, industry blogs), earning high‑quality embeds and backlinks.

Package it as a repeatable agency offer

To make this work as an agency service, productize the workflow and report on discoverability and outcomes, not vanity “SEO scores”.

Position your “YouTube SEO” not as thumbnail tweaks but as a strategic, entity-driven discoverability engine that connects YouTube content to your client’s entire marketing and authority stack.

The post How to build your YouTube SEO by a Top Expert Agency appeared first on Primary Position SEO NYC.

And they dont go hand-in-hand with Page Titles. You can leave them blank

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When you type a question into an LLM with web access, it does not just “think harder” with its training data. It typically:

• Expands your question into a cluster of related sub-queries (query fan-out) to cover entities, intents, and follow-up angles a human might ask next​

• Sends those sub-queries through retrieval layers that look like a hybrid of keyword search, vector search, and metadata filters.​

• Pulls back chunks of documents from an index, then re-ranks those chunks for relevance, authority, and freshness before generating an answer and, sometimes, citations​

In simple terms, you get cited when your pages are the best matching and most trusted chunks in the index for the fan-out queries the system created, not just the exact words a user typed.

The core inputs that matter

Several recurring signals show up across analyses of LLM citations and AI-overview style answers:

• Ranking in classic search: Studies that pulled large samples of LLM citations found strong correlation between appearing on the first page of Google/Bing and being mentioned or cited by models.​

• Backlinks and PageRank-style authority: Link-based authority still shapes which pages rise into those top positions and into AI answers indirectly, even when raw backlink count is not the only variable.​

• Freshness: Content updated recently, especially within the last 30–90 days, tends to show a visibility boost in AI responses versus older, stale pages.​

• Coverage depth: Comprehensive, well-structured content that answers not only the seed query but also the fan-out cluster of related questions tends to win more AI mentions and citations.​

So, the “secret” is not a special LLM switch. It is an evolved version of search fundamentals: authority, freshness, and depth mapped to the way AI decomposes queries.

Practical steps to get cited

If the goal is citations and mentions inside LLMs, your strategy needs to be built around how retrieval and fan-out work in practice:

• Map the fan-out around your topics: Use keyword tools, “People Also Ask,” internal search data, and customer conversations to list the natural follow-up questions around each core topic.​

• Build cluster pages that answer the entire intent space: Instead of thin, single-keyword posts, create assets that handle definitions, use cases, comparisons, pitfalls, and implementation details in one coherent structure.​

• Structure for indexing and chunking: Use clear headings, semantic HTML, and concise sections so retrieval systems can split your content into meaningful chunks and tag them correctly with metadata.​

• Invest in real link-worthy content and promotion: Earn authoritative links through original data, strong opinions, or practical frameworks that people actually want to reference. These links still feed the authority metrics that lift you into both SERPs and AI answers.e​

• Keep key assets fresh: Update important pieces regularly with new examples, data, and clarifications so they stay at the front of freshness-sensitive rankings and LLM indices.​

This is less about chasing the model and more about making your content the obvious retrieval candidate in any system that cares about intent coverage, authority, and clarity.

Myths vs. facts about LLM visibility

A lot of half-true claims float around. It helps to separate them into myths, partial truths, and durable facts.

Common myths

• “You need a special LLM SEO layer completely separate from normal SEO.”
  In reality, most documented LLM citation analyses show that being strong in classic rankings correlates heavily with being visible in AI answers; the systems share indices and signals even if the last-mile generation is new.​

• “Traffic and brand size alone drive AI citations.”
  Reviews of citation patterns show that smaller sites with strong topical authority and clean link profiles can be cited frequently, while big brands without depth or clarity on a topic can be underrepresented.​

• “Schema or entity markup is the magic key.”
  Structured data helps with indexing, interpretation, and traceability, but it is not a standalone ranking or citation button; it supports the underlying retrieval and relevance signals rather than replacing them.​

• “Dwell time is the main behavioral signal that boosts you into LLM answers.”
  Dwell time has been heavily debated and repeatedly downplayed as a reliable direct ranking factor; its impact appears far weaker and more indirect than people claim.​

Nuanced or partial truths

• “Click-through rate is a ranking factor.”
  User interaction data, including CTR, shows up in leaked and trial-exposed documents as part of systems that adjust search rankings based on how often results are clicked relative to expectations.​
  It is not the only signal and is noisy, but earning above-expected CTR can reinforce your relevance and, by lifting your rankings, indirectly improve your LLM visibility as well.​

• “Backlinks don’t matter anymore for AI.”
  The raw count of links is less predictive on its own, but link-based authority and link acquisition patterns still feed into the ranking and trust systems underneath AI search.​
  For LLMs specifically, high-quality mentions (linked or unlinked) in authoritative environments, plus solid link profiles, correlate with more frequent citations.​

• “LLMs will always pick the top organic result.”
  LLMs sometimes pull from deeper pages when those pages address specific sub-questions better than the page ranked first for the head term.​
  Being number one for the main keyword helps, but covering niche sub-intents thoroughly can win you citations even when you sit lower on the primary SERP.

Reliable facts

• LLMs depend on indices and retrieval layers.
  They lean on structured indices, vector stores, and ranking components that determine which chunks they see before they generate answers. If you are not indexed cleanly and ranked reasonably well, your odds of being cited are low.​

• Query fan-out defines the real battleground.
  Visibility in AI search depends on whether you rank for the many sub-queries the system spins out, not just the surface query users type. That makes coverage of question clusters and entity relationships central.​​

How to think about strategy going forward

If you live on Reddit and similar communities, you are already tuned into where unlinked brand mentions and topical authority are created, which LLMs can treat as soft signals even when there is no direct link. The opportunity is to bridge that with a site architecture and content strategy designed for fan-out, retrieval, and classic ranking.​

That means:

• Choosing topics where you can realistically become the best, deepest explainer in the index.​

• Designing pages that answer not just “the question” but the web of follow-ups an LLM will spin out.​

• Building enough authority and freshness that, when the system goes looking, your chunks are always in the short list of candidates.​

Do that consistently over time, and citations stop looking like magic and start looking like the natural byproduct of how these systems are engineered.

The post How doe AI LLMS actually cite or mention your brand? appeared first on Primary Position SEO NYC.

LLMs are not search engines, and treating them like they are leads to bad strategy and worse content decisions. A whole layer of “AI SEO” advice is built on this confusion.

LLMs are not search engines

LLMs like ChatGPT, Gemini, Claude, or Perplexity are language models: they generate text by predicting the next token based on patterns in their training data or retrieved snippets. They are not end-to-end systems that crawl, index, and rank the public web. A true search engine has three core components: a crawler that discovers URLs, an index that stores and structures documents, and a ranking algorithm that orders results based on signals like links, relevance, and freshness. LLMs have none of those pieces built in.

When an “AI assistant” appears to search the web, what is really happening is orchestration: a search backend (Google, Bing, Brave, custom vertical indexes, etc.) runs the queries, returns documents, and the LLM summarizes or reformats those documents into a conversational answer. The ranking power—and the economic stakes—still live in that retrieval layer, not in the model’s text generator. That is where PageRank-style link graphs and query–document relevance actually work.

No search index, no ranking algorithm

Because LLMs are not search engines, they also do not maintain a continuously updated search index in the way Google or Bing do. A model’s “knowledge” is either frozen at training time (its weights) or borrowed, on demand, from external tools that actually manage indexes. There is no internal, live database of URLs and documents being re-ranked for every query.

That has two implications for SEO and content strategy. First, there is no hidden “LLM-native” ranking algorithm you can game the way people once tried to game early web search. Second, the only ranking system that matters for AI surfaces pulling from the live web is still the underlying search engine’s algorithm. If your page does not rank and attract links and engagement there, it is unlikely to be in the pool of candidates an LLM can even see, let alone cite.

“By default” they are not search engines

A common myth is that “any LLM is a search engine by default.” It sounds logical—ask a question, get an answer—but it is technically wrong. An offline LLM with no tools enabled cannot browse, cannot click links, and cannot access fresh documents. It can only remix what it has seen during training. Calling that “search” is like calling a trivia buff with an old encyclopedia a real-time newswire.

Modern AI products bolt search capabilities onto models via tools, APIs, and retrieval pipelines. Those are separate systems with their own logic, infrastructure, and failure modes. That distinction matters: if you confuse “answer generator” with “search engine,” you will misattribute ranking decisions to the model’s personality instead of the very real ranking systems underneath it.

They don’t need “clear structure” or special prose

Another myth is that LLMs “prefer” certain writing styles, ultra-clear H2/H3 hierarchies, or a specific brand of “AI-friendly” tone. That is a category error. Models consume token streams, not “pretty pages.” They happily ingest messy text, code, tables, and user comments. Their job is to compress patterns across all of it, not to reward one stylistic choice with extra visibility.

Clear structure is absolutely useful—for humans, and for search engines that parse headings, snippets, and structured data to understand context and build SERP features. But an LLM is not “more likely to pick your article” just because you wrote at an eighth-grade reading level or used some magic formatting. If the underlying search ranking system does not surface your page near the top for relevant queries, the model is unlikely to ever see it, regardless of how “AI-friendly” the prose feels to copywriters.

Schema: useful for search, not an LLM ranking hack

Schema markup is another area where confusion sets in. Mark Williams-Cook and other practical SEOs have repeatedly pointed out that schema helps search engines understand entities, relationships, and intent, especially for rich results and verticals like jobs, products, and events. It is a strong hinting mechanism, not a standalone ranking cheat code.

LLMs, however, do not “care” about schema in the sense that people claim. When schema is exposed in the HTML that a crawler or retriever sees, it can help the search stack classify and feature your content; the LLM then benefits indirectly by having cleaner, more structured inputs to summarize. But the model is not scanning your JSON-LD and thinking, “This page used FAQ schema correctly, therefore I will rank it higher in my answer.” It is simply seeing whatever documents the retrieval layer passes along. If schema helped those documents rank, great—but that is a search engine win, not an LLM preference.

What actually matters in the AI/LLM era

If LLMs are not search engines, what matters for visibility in AI-overview, answer cards, and chat-style SERPs?

• Classic ranking still rules
  Links, authority, and query–document relevance determine which pages sit in the top tiers of the index. Those are the pages AI layers pull from when constructing answers. If you are not in that set, your odds of being cited or summarized drop dramatically.

• Retrieval design, not “LLM SEO” hacks
  The way an AI product fans out queries, selects documents, and deduplicates sources determines which URLs are seen. None of that logic lives inside the model’s prose style; it lives in the retrieval, ranking, and aggregation stack.

• Human-centric clarity, not model-centric superstition
  Invest in clear information architecture, honest titles, tight intros, and well-structured answers because users and search engines benefit—not because an LLM “likes” it. When humans understand and stay on your page, the behavioral and linking signals that feed real ranking systems improve.

The bottom line: LLMs are answer engines sitting on top of search, not replacements for it. They do not have their own public-web index, they do not run their own link-based ranking algorithms, and they do not reward magical “AI-friendly” writing formulas or schema tricks. If you want AI visibility, optimize for the thing that actually ranks and gets retrieved: the search engine underneath.

The post The Truths in LLM Marketing appeared first on Primary Position SEO NYC.

There’s a pervasive lie in the digital marketing industry — a slogan that’s been costing businesses millions in wasted investment for nearly three decades. You’ve heard it, repeated at conferences, webinars, and in every “Top 10 SEO Tips” article since 1996.

“Content is King.”

The implication is as lazy as it is costly: if you write it, they will come.
If you just produce “high-quality,” “engaging,” or “valuable” content, Google will magically reward you with visibility and leads.

It won’t.
That belief is a myth — one that refuses to die because it excuses marketers from doing the hard, unglamorous work that actually moves rankings.

As someone who’s spent 15+ years dissecting search algorithms (and debating SEO’s voodoo lore in places like Reddit’s r/SEO under the handle Weblinkr), I can tell you unequivocally:

Content does not market itself.

You can hire the best copywriter alive to craft the smartest, most insightful analysis on “Cloud Migration Strategies.” But if you publish it on a domain with zero authority, no internal structure, and no link equity — it will die unseen. It won’t rank. It won’t bring leads. It will simply exist — a perfect article lost in a mathematical system that never found it.

Let’s break down the physics of why.

Google Is a Calculator, Not a Critic

Marketers love to talk about “content quality,” as if Google were an English professor grading essays. They imagine an algorithmic editor that reads, understands, and rewards great writing for its brilliance.

That’s not how it works.

Google doesn’t understand prose. It measures it — using statistical and mathematical proxies for relevance, authority, and usefulness. Every ranking signal is a math function trying to simulate human judgment.

Quality, in the human sense, is subjective.
But Google doesn’t optimize for subjectivity. It optimizes for PageRank.

The PageRank Reality

Despite endless “SEO is Dead” headlines, PageRank still underpins the modern web. Its core principle is brutally simple:

Links are votes.

• A link from a high-authority site is a vote that says, “This page matters.”

• A link from a niche-relevant site says, “This page is relevant in this context.”

Without backlinks, your page has no mathematical weight.
You can write the “best” content in the world, but without votes — you’re invisible. PageRank doesn’t crown kings for literary style.
It crowns them for link velocity and authority flow.

Your masterpiece without links? It’s a peasant in a system that only respects hierarchy.

The Field of Dreams Fallacy

This is where “Content Marketing” becomes a farce. The term itself is misleading. It implies that content performs the marketing — that simply hitting “publish” is a form of promotion.

That’s the Field of Dreams Fallacy: if you build it, they will come.

They won’t.
Not in 2025, when millions of posts go live every day.

Building content is easy.
Getting it seen is the hard part.
And in a world already flooded with “great content,” distribution — not writing — is the true differentiator.

If 90% of your marketing budget goes into creating content and only 10% into promoting it, you’re burning money.
Flip that ratio, and you’ll outperform competitors still clinging to the myth of organic discovery.

Context Is King

If “Content is King” is a myth, what’s the truth?

Context is King.

Google isn’t trying to reward “the best written” page. It’s trying to return the page that best satisfies user intent.

The Intent Trap

Most businesses fail at SEO because they write for keywords instead of users. They publish 2,000-word “educational” posts for every term, hoping something sticks.

We call this the “spaghetti strategy.”

At Primary Position, we categorize user intent like this:

• Tier 1: Commercial — Searchers ready to buy. (“Enterprise SEO Agency NYC,” “SaaS link building services.”)
  These demand landing pages, not essays.

• Tier 2: Informational — Searchers researching. (“How does PageRank work?”)
  These deserve answers, not pitches.

Most companies get this backward — writing “thought leadership” pieces for users who just wanted a quote form. Every second a solution hides under 1,000 words of fluff, you lose the user.

Don’t “blog.” Answer intent.

If they want a calculator — build it.
If they want a definition — provide it.
If they want a service — sell it.
Rankings follow satisfaction, not storytelling.

Technical Structure Beats Creative Flow

Here’s another inconvenient truth:
Your web developer is more important to your SEO than your copywriter.

Why? Because if Googlebot can’t crawl your content, it doesn’t exist.

• Schema Markup: Label your assets so Google knows what they are — Article, Review, FAQ, Product.

• Internal Linking: Build deliberate link architecture. Pass authority like a network engineer, not a blogger.

• Crawl Budget: Keep your site lean. If you waste crawl cycles on bloat, your important assets never get indexed.

Search engines reward clarity, not creativity. Crawl efficiency isn’t sexy — but it’s what makes ranking predictable.

The Propulsion System

So, if content doesn’t market itself, what does?

You need a Propulsion System.

1. Foundation (Technical): Build a fast, clean, crawlable site.

2. Asset (Content): Publish material aligned directly to user intent — not vanity metrics like “read time” or “word count.”

3. Fuel (Links): Acquire real backlinks — through Digital PR, studies, outreach, or partnerships. Not spammy marketplaces.

Content is just the engine.
Links are the fuel.
Technical SEO is the infrastructure.
Without all three, nothing moves.

Stop Waiting to Be Discovered

The web is no longer a meritocracy.
Good ideas don’t automatically rise. Algorithms don’t reward romance — they reward math.

The sooner you accept that truth, the faster you’ll win.

Stop waiting to be discovered.
Stop blogging into the void.
Stop believing “content marketing” is about writing.

Start building systems that force discovery.
Respect the math.
Own your distribution.
That’s how modern SEO works.

The post The Big “Content Marketing” Myth: Content Doesn’t Market Itself appeared first on Primary Position SEO NYC.